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Wenjie Ruan

Wenjie Ruan has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Bias, fairness & safety; often with Changshun Wu, Wei Huang, Xiaowei Huang.

2 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 24 · 123 works · 3,198 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 41 times in our library · ⭐ Paper Stars ranking: #6480

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of LiverpoolLiverpool · United Kingdom2023–2026 · 18 paperscurrent🎓University of ExeterExeter · United Kingdom2012–2025 · 46 papers🎓The University of AdelaideAdelaide · Australia2014–2018 · 22 papers🎓University of OxfordOxford · United Kingdom2017–2019 · 10 papers🎓Lancaster UniversityLancaster · United Kingdom2020 · 6 papers🎓University of Science and Technology of ChinaHefei · China2025–2026 · 2 papers🎓Central South UniversityChangsha · China2013–2016 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksEngineering · 1 workArtificial Intelligence · 2 worksSafety, Risk, Reliability and Quality · 1 workSoftware · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Bias, fairness & safety · 2 worksLanguage models & pretraining · 2 works

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation (2023 · 199 citations)
    The survey examines how to make large language models safe and trustworthy as they move quickly into industrial and consumer use.
  2. Defending LLMs against Jailbreak Attacks via Template-Based ICL with a Defensive Suffix (2026)
    Jailbreak prompts can push aligned LLMs into producing harmful content. Existing defenses have drawbacks: fine-tuning is costly and needs access to model weights, and input or output filters often generalize poorly. The authors propose a lightweight plug-in defense (code: DSICL) with two parts.

 Co-authors here

4 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 41 times (of the 149,439 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated

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